| Autor: |
Kanani, Pratik, Vasoya, Anil, Shah, Kamal, Kothari, Neel, Patil, Nilesh, Pandya, Gayatri, Padole, Mamta |
| Předmět: |
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| Zdroj: |
International Journal of Intelligent Engineering & Systems; 2024, Vol. 17 Issue 1, p403-417, 15p |
| Abstrakt: |
The healthcare industry relies on efficient and fast decision making. This paper aims to expand the Fog computing and Distributed computing domains to optimize quality of service (QoS) in order to facilitate IoT based healthcare applications with low latency requirements and developing a smart fog gateway equipped with an optimized fog algorithm. The purpose of this study is to optimize real-time healthcare data processing using Fog computing, ensuring dependable, rapid decision-making while minimizing delays caused by data transmission and computation. This is also known as Health-as-a-service (HaaS). We conduct an electrocardiography (ECG) analysis utilizing three computing paradigms: Cloud computing, Fog computing, and a heterogeneous distributed Fog computing setup employing the dynamic OptiFog algorithm. This algorithm effectively manages computational resources within the distributed Fog environment, utilizing Raspberry Pi clusters to enhance performance during worst-case scenarios. The response time is measured using Short Message Service (SMS). The OptiFog node exhibited a response better than the Fog node and the cloud node. The OptiFog algorithm not only takes into account different computing parameters like number of cores, memory usage, CPU utilization and response time of the computing node but also assigns dynamic priorities to these parameters to get the best possible processing available. Based on the workload of the task/node, it dynamically decides the job size to save the network bandwidth and to reduce the network overhead. In conclusion, the proposed work demonstrates that optimizing Fog computing with the dynamic OptiFog algorithm is an effective approach to meet low-latency requirements in IoT-based healthcare applications making it a valuable addition to the Health-as-a-Service (Haas) framework for real-time healthcare data processing. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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